252 citations · 529 across the 19 of their papers we have counts for
13 papers · 1 filter
Technical Question Answering across Tasks and Domains
Wenhao Yu, Lingfei Wu, Yu Deng +4
Building automatic technical support system is an important yet challenge task. Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve r…
A Survey of Knowledge-Enhanced Text Generation
Wenhao Yu, Chenguang Zhu, Zaitang Li +4
The goal of text generation is to make machines express in human language. It is one of the most important yet challenging tasks in natural language processing (NLP). Since 2014, v…
Federated Dynamic GNN with Secure Aggregation
Meng Jiang, Taeho Jung, Ryan Karl +1
Given video data from multiple personal devices or street cameras, can we exploit the structural and dynamic information to learn dynamic representation of objects for applications…
Injecting Entity Types into Entity-Guided Text Generation
Xiangyu Dong, Wenhao Yu, Chenguang Zhu +1
Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonst…
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
Daheng Wang, Zhihan Zhang, Yihong Ma +4
Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the no…
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
Daheng Wang, Meng Jiang, Munira Syed +4
User behavior modeling is important for industrial applications such as demographic attribute prediction, content recommendation, and target advertising. Existing methods represent…
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
Tianwen Jiang, Tong Zhao, Bing Qin +3
Noun phrases and relational phrases in Open Knowledge Bases are often not canonical, leading to redundant and ambiguous facts. In this work, we integrate structural information (fr…
A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction
Yang Zhou, Tong Zhao, Meng Jiang
Textual patterns (e.g., Country's president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction m…
Data Augmentation for Graph Neural Networks
Tong Zhao, Yozen Liu, Leonardo Neves +3
Data augmentation has been widely used to improve generalizability of machine learning models. However, comparatively little work studies data augmentation for graphs. This is larg…
Crossing Variational Autoencoders for Answer Retrieval
Wenhao Yu, Lingfei Wu, Qingkai Zeng +3
Answer retrieval is to find the most aligned answer from a large set of candidates given a question. Learning vector representations of questions/answers is the key factor. Questio…
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning
Mandana Saebi, Steven Krieg, Chuxu Zhang +2
Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fa…
Enhancing Factual Consistency of Abstractive Summarization
Chenguang Zhu, William Hinthorn, Ruochen Xu +4
Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applic…
Improving Generalizability of Fake News Detection Methods using Propensity Score Matching
Bo Ni, Zhichun Guo, Jianing Li +1
Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this pa…